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When compared to fault-tolerant quantum computational strategies, variationalquantum algorithms stand as one of the candidates with the potential ofachieving quantum advantage for real-world applications in the near term.However, the optimization of the circuit parameters remains arduous and isimpeded by many obstacles such as the presence of barren plateaus, many localminima in the optimization landscape, and limited quantum resources. Anon-random initialization of the parameters seems to be key to the success ofthe parametrized quantum circuits (PQC) training. Drawing and extending ideasfrom the field of meta-learning, we address this parameter initialization taskwith the help of machine learning and propose FLIP: a FLexible Initializer forarbitrarily-sized Parametrized quantum circuits. FLIP can be applied to anyfamily of PQCs, and instead of relying on a generic set of initial parameters,it is tailored to learn the structure of successful parameters from a family ofrelated problems which are used as the training set. The flexibility advocatedto FLIP hinges in the possibility of predicting the initialization ofparameters in quantum circuits with a larger number of parameters from thoseused in the training phase. This is a critical feature lacking in othermeta-learning parameter initializing strategies proposed to date. We illustratethe advantage of using FLIP in three scenarios: a family of problems withproven barren plateaus, PQC training to solve max-cut problem instances, andPQC training for finding the ground state energies of 1D Fermi-Hubbard models.

FLIP: A flexible initializer for arbitrarily-sized parametrized quantum circuits